In Multi-Agent Reinforcement Learning (MARL), multiple agents act interactively and complete tasks in a sequential decision-making manner with Reinforcement Learning (RL). It has made remarkable advances in many domains, including autonomous systems [1]}, [2]}, [3]} and real-time strategy (RTS) video games [4]}.
By the virtue of the centralised training with decentralised execution (CTDE) [5]} paradigm, which aims to tackle the scalability and partial observability challenges in MARL, many CTDE-based MARL methods are proposed [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}. With these methods, an agent executes actions only via feeding its individual observations independently and optimizes its own policy with access to global trajectories centrally.
